A robust and efficient clustering algorithm based on cohesion self-merging
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
Novel Hybrid Hierarchical-K-means Clustering Method (H-K-means) for Microarray Analysis
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Hierarchical initialization approach for K-Means clustering
Pattern Recognition Letters
Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters
IEEE Transactions on Knowledge and Data Engineering
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Hierarchical K-means clustering is one of important clustering task in data mining. In order to address the problem that the time complexity of the existing HK algorithms is high and most of algorithms are sensitive to noise, a hierarchical K-means clustering algorithm based on silhouette and entropy(HKSE) is put forward. In HKSE, the optimal cluster number is obtained through calculating the improved silhouette of the dataset to be clustered, so that time complexity can be reduced from O(n2) to O(k × n). Entropy is introduced in the hierarchical clustering phase as the similarity measurement avoiding distance calculation in order to reduce outlier effect on the cluster quality. In the post processing phase, the outlier cluster is identified by computing the weighted distance between clusters. Experiment results show that HKSE is efficient in reducing time complexity and sensitivity to noise.